GitLab

* discovery: only retry AMD GPUs

CUDA and Vulkan don't crash on unsupported devices, so retry isn't necessary.
This also refactors the code to shift the Library specific logic into the ml
package.

* review comments

* Fix vulkan PCI ID and ID handling

Intel GPUs may not report PCI IDs which was leading to incorrect overlap
detection.  Switch to using the existing PCI IDs, however AMD GPUs claim not to
report PCI IDs, but actually do, so try anyway, as this is required for ADLX to
find the GPUs on Windows. Numeric IDs lead to scheduling problems, so this also
switches Vulkan to use UUID based IDs. The GPU discovery patches have been
squashed into a single patch to simplify future rebases.

* review comments

* DRY out the runner lifecycle code

Now that discovery uses the runners as well, this unifies the runner spawning code
into a single place.  This also unifies GPU discovery types with the newer ml.DeviceInfo

* win: make incremental builds better

Place build artifacts in discrete directories so incremental builds don't have to start fresh

* Adjust sort order to consider iGPUs

* handle cpu inference oom scenarios

* review comments

* implement the vulkan C backend

* add support in gpu.go

* add support in gen_linux.sh

* it builds

* fix segfault

* fix compilation

* fix free memory monitor

* fix total memory monitor

* update gpu.go

* fix build

* fix check_perfmon len

* remove cap_get_bound check

* fix vulkan handle releasing

* fix build on federa 40

* fix vulkan on windows

* making amdgpu work on arm achitecutre with vulkan

* add x86_64 lines in VulkanGlobs and capLinuxGlobs

* add aarch64 lines in vulkanGlobs and capLinuxGlobs

* Fix variable name

* Add vulkan build patch from @jmorganca

* Sync vendored ggml to add Vulkan support

* Updated dockerfile

https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871

Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* Installing rocm library
Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* This version works well

built based on this: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871

Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* Applied 00-fix-vulkan-building.patch

Work done by McBane87 here: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871

Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* Fixed the "detached head" issues
Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* Merged in the right direction
Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* Merging the latest stable (#2)

* Applied 00-fix-vulkan-building.patch

* Implemented vulkan backend based on the work done by whyvl, Dts0, McBane87 and others

Tested on AMD Ryzen 7 8845HS w/ Radeon 780M Graphics with ROCm disabled

```
[GIN-debug] POST   /v1/chat/completions      --> github.com/ollama/ollama/server.(*Server).ChatHandler-fm (6 handlers)
[GIN-debug] POST   /v1/completions           --> github.com/ollama/ollama/server.(*Server).GenerateHandler-fm (6 handlers)
[GIN-debug] POST   /v1/embeddings            --> github.com/ollama/ollama/server.(*Server).EmbedHandler-fm (6 handlers)
[GIN-debug] GET    /v1/models                --> github.com/ollama/ollama/server.(*Server).ListHandler-fm (6 handlers)
[GIN-debug] GET    /v1/models/:model         --> github.com/ollama/ollama/server.(*Server).ShowHandler-fm (6 handlers)
time=2025-03-11T13:00:40.793Z level=INFO source=gpu.go:199 msg="vulkan: load libvulkan and libcap ok"
time=2025-03-11T13:00:40.877Z level=INFO source=gpu.go:421 msg="error looking up vulkan GPU memory" error="device is a CPU"
time=2025-03-11T13:00:40.878Z level=WARN source=amd_linux.go:443 msg="amdgpu detected, but no compatible rocm library found.  Either install rocm v6, or follow manual install instructions at https://github.com/ollama/ollama/blob/main/docs/linux.md#manual-install"
time=2025-03-11T13:00:40.878Z level=WARN source=amd_linux.go:348 msg="unable to verify rocm library: no suitable rocm found, falling back to CPU"
time=2025-03-11T13:00:40.879Z level=INFO source=types.go:137 msg="inference compute" id=0 library=vulkan variant="" compute=1.3 driver=1.3 name="AMD Radeon Graphics (RADV GFX1103_R1)" total="15.6 GiB" available="15.6 GiB"
```

```
 # ollama run phi4:14b
>>> /set verbose
Set 'verbose' mode.
>>> how's it going?
Hello! I'm here to help you with any questions or tasks you have. How can I assist you today? 😊



total duration:       3.341959745s
load duration:        18.165612ms
prompt eval count:    15 token(s)
prompt eval duration: 475ms
prompt eval rate:     31.58 tokens/s
eval count:           26 token(s)
eval duration:        2.846s
eval rate:            9.14 tokens/s
>>>
```

* This is no longer needed
Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* Fixes SIGSEGV: segmentation violation running gemma3 models on ollama 0.6.0 #21

Patch provided by McBane87 on https://github.com/whyvl/ollama-vulkan/issues/21

Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* Applied 04-disable-mmap-vulkan.patch

From: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871

Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* Pulled new upstream code for ggml-bulkan backend
Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* Merged latest ollama 0.6.2 and nasrally's Flash Attention patches (#5)

* readme: add Ellama to list of community integrations (#9800)

* readme: add screenpipe to community integrations (#9786)

* Add support for ROCm gfx1151 (#9773)

* conditionally enable parallel pipelines

* sample: make mutations in transforms explicit (#9743)

* updated minP to use early exit making use of sorted tokens

* ml/backend/ggml: allocate memory with malloc when loading model (#9822)

* runner: remove cache prompt flag from ollama runner (#9826)

We do not need to bypass the prompt caching in the ollama runner yet, as
only embedding models needed to bypass the prompt caching. When embedding
models are implemented they can skip initializing this cache completely.

* ollamarunner: Check for minBatch of context space when shifting

Models can specify that a group of inputs need to be handled a single
batch. However, context shifting didn't respect this and could trigger
a break anyways. In this case, we should instead trigger a context
shift earlier so that it occurs before the grouped batch.

Note that there still some corner cases:
 - A long prompt that exceeds the context window can get truncated
   in the middle of an image. With the current models, this will
   result in the model not recognizing the image at all, which is
   pretty much the expected result with truncation.
 - The context window is set less than the minimum batch size. The
   only solution to this is to refuse to load the model with these
   settings. However, this can never occur with current models and
   default settings.

Since users are unlikely to run into these scenarios, fixing them is
left as a follow up.

* Applied latest patches from McBane87

See this for details: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2708820861

Signed-off-by: Vadim Grinco <vadim@grinco.eu>

* Add ability to enable flash attention on vulkan (#4

)

* discover: add flash attention handling for vulkan
* envconfig: fix typo in config.go

As part of the process some code was refactored and I added a new field
FlashAttention to GpuInfo since the previous solution didn't allow for a
granular check via vulkan extensions. As a side effect, this now allows
for granular per-device FA support checking in other places

---------
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
Co-authored-by: zeo <108888572+zeozeozeo@users.noreply.github.com>
Co-authored-by: Louis Beaumont <louis.beaumont@gmail.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
Co-authored-by: Michael Yang <mxyng@pm.me>
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Nikita <50599445+nasrally@users.noreply.github.com>

* Revert Readme changes

* Revert

* Revert changes in amd_linux.go

* Revert changes in amd_linux.go

* Remove flashattention setting gpu.go

* Revert whitespace changes in gpu.go

* Revert changes in transforms_test.go

* Revert changes in runner.go

* Revert changes in Makefile.sync

* Revert some unintented changes in Dockerfile

* Revert vulkan copy changes in Dockerfile

* Update Vulkan Code to de4c07f93783a1a96456a44dc16b9db538ee1618

* Fixed duplicate sync in ggml.go

* Revert changes in ggml.go

* Revert chnages in ggml.go

* enable falsh attention on vulkan

* revert remove parenthesis

* fixed flash attention logic enabling

* vk_check_flash_attention 0 means supported

* Update gpu.go

* Add vulkan to Windows Build script

* Remove commented out code

* Enable Vulkan Flash attention in FlashAttentionSupported

* Fix logging

* Update Vulkan backend to e54d41befcc1575f4c898c5ff4ef43970cead75f

* Removed libcap related code

libcap is not directly related to Vulkan and should be added by its own PR. It adds additional library dependencies for building and also requires users to run setcap or run ollama as root, which is not ideal for easy use

* Fix Unit Test (Add Vulkan Library)

* Add vulkan to TestHomogeneousGPUs
Test

* vulkan: get GPU ID (ollama v0.11.5)
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

* disable mmap for vulkan

* Reduce Changes remove TestHomogeneousGPUs (doesn't exist on master)

* Update vulkan version to the version used in llama.cpp

* rename gpu patch to correct number

* added Vulkan API to get correct Device UUID

current UUID from pipelineCacheUUID does not match CUDA

* Fix GPU ID Patch

* Remove Code not in llama.cpp

* modified UUID code inside ggml

* Fix Patch

* Copied minimal definition from vulkan header

* Fix compile error in Mac

Metal is preferred so we're disabling Vulkan for now

* Removed unused code

Fix linter error in CI

* Fix patches apply

* fixing lint error

* Removed unneeded function call

Somehow removing this call fixed the crashing when Vulkan header was removed

* added missing NL

* Fixed missing members in Vulkan header

also added zero clear for some structs

* Fixed wrong structure ID

* Fixed Vulkan header

More aligned with official header definition now

* buildvulkanAsSeperateFunction

* Vulkan on Windows Test

* temporarly comment out gate to run windows task

* use temporarly windows-latest for build

* Commenting out other presets to build vulkan

* reenable cpu

* commenting out error action stop

* temporarly commenting out rocm

* set vulkan path

* comment out cude for faster turnaround

* correct vulkan install

* correct vulkan silent install

* fixed install command

* revert debugging changes (vulkan builds on windows)

* revert windows-latest

* trying to build vulkan for linux

* temporarly disable cuda and rocm

* try again linux build

* fix version

* trying to fix

* trying again

* trying again

* fix version

* fixed vulkan-sdk name

* try again

* trying again

* try without version number

* try again

* add some more extra

* trying to use version 1.4.313

* revert debugging changes

* Filter out already supported gpus

* revert debug code

* Use runners for GPU discovery

This revamps how we discover GPUs in the system by leveraging the Ollama
runner.  This should eliminate inconsistency between our GPU discovery and the
runners capabilities at runtime, particularly for cases where we try to filter
out unsupported GPUs.  Now the runner does that implicitly based on the actual
device list.  In some cases free VRAM reporting can be unreliable which can
leaad to scheduling mistakes, so this also includes a patch to leverage more
reliable VRAM reporting libraries if available.

Automatic workarounds have been removed as only one GPU leveraged this, which
is now documented. This GPU will soon fall off the support matrix with the next
ROCm bump.

Additional cleanup of the scheduler and discovery packages can be done in the
future once we have switched on the new memory management code, and removed
support for the llama runner.

* timing info for runner

* WIP - wire up Vulkan with the new engine based discovery

Not a complete implementation - free VRAM is better, but not accurate on
windows

* fix - trust the library paths from discovery when starting runner

* fix index bug

* fix vulkan ids to be underlying

* fix - give bootstrapping more time on slow systems

* Test if Vulkan device is supported

* vk_check_flash_attention is not needed (coompat2 coopmapt and scalar implementation exist)

* Handle GGML_VK_VISIBLE_DEVICES

* ask for supported first

* win: fix CPU query buffer handling

Try in a short loop until we get the size right.

* test: harden integration tests for slow start

If the server takes a while to start up, block
tests from starting until it's online to avoid
setting large timeouts in individual test cases.

* gofumpt fix

* fix build

* merge fixes

* merge fixes

* fixed build

* merge fixes

* fixing build

* fixed build

* fixed formatting

* fixed build

* fix vulkan gpu id patch

* sync llama.cpp vulkan code

* update build windows script

* merge fixes

* fix format

* fixed vulkan casing

* handle igpu as gpu

* improve case

* print out unknown library

* rturn Vulkan for vulkan library

* Revert "rturn Vulkan for vulkan library"

This reverts commit 690461a12fd5e93295d174c97edefb2bc33285b1.

* fixed patch number

* return Library Name

* remvoe debug code

* return integrated in vulkan backend

* Return pci Properties

* update patch

* directly get pci proeprties without parsing

* workaround for filtering devices. Correct way is to have a LibraryPosition Parameter in the deviceInfo

* Revert "directly get pci proeprties without parsing"

This reverts commit 8e0624851f5ed7d9f74518f574dfb422e4dd4dc2.

* Set FilteredID for Environment Filtering

* ROCm Library is named ROCm

* revert changes in patch

* Create 0028-vulkan-pci-and-memory.patch

* vulkan memory patch

* casing fix

* Add more pci properties

* Added better memory management

* Added better memory managament

* fixed patch

* Fixed patch

* FilterID creation group by library

* filter out vulkan supported by other gpu

* fixing deviceid compare

* Vulkan Fix FA coopmat1 invalid array indexing

* Use everywhere the same Vulkan Version 1.4.321.1

* Remove unneeded patch

* vulkan update

* sync vulkan glsl files

* only use for vulkan the filteredid (numeric device number)

* simplify code

---------
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
Co-authored-by: pufferffish <github@bandersnatch.anonaddy.com>
Co-authored-by: KOISHI KOMEIJI FROM TOUHOU 11 <fuck>
Co-authored-by: DSLstandard <qgeneral35@gmail.com>
Co-authored-by: pufferffish <me@windtfw.com>
Co-authored-by: yeongbba <yeongmo.lee@logpresso.com>
Co-authored-by: tomaThomas <tomathomas@mailbox.org>
Co-authored-by: Antoine Viallon <antoine@lesviallon.fr>
Co-authored-by: Vadim Grinco <vadim@grinco.eu>
Co-authored-by: zeo <108888572+zeozeozeo@users.noreply.github.com>
Co-authored-by: Louis Beaumont <louis.beaumont@gmail.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
Co-authored-by: Michael Yang <mxyng@pm.me>
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Nikita <50599445+nasrally@users.noreply.github.com>
Co-authored-by: Masato Nakasaka <masato.nakasaka@intel.com>
Co-authored-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>

GGML picks the wrong kernel and these systems fail with:
Sep 28 22:25:39 xavier ollama[48999]: //ml/backend/ggml/ggml/src/ggml-cuda/fattn-wmma-f16.cu:437:
ERROR: CUDA kernel flash_attn_ext_f16 has no device code compatible with CUDA arch 720. ggml-cuda.cu
was compiled for: __CUDA_ARCH_LIST__

Fixes #12442

This revamps how we discover GPUs in the system by leveraging the Ollama
runner.  This should eliminate inconsistency between our GPU discovery and the
runners capabilities at runtime, particularly for cases where we try to filter
out unsupported GPUs.  Now the runner does that implicitly based on the actual
device list.  In some cases free VRAM reporting can be unreliable which can
leaad to scheduling mistakes, so this also includes a patch to leverage more
reliable VRAM reporting libraries if available.

Automatic workarounds have been removed as only one GPU leveraged this, which
is now documented. This GPU will soon fall off the support matrix with the next
ROCm bump.

Additional cleanup of the scheduler and discovery packages can be done in the
future once we have switched on the new memory management code, and removed
support for the llama runner.

* Always filter devices

Avoid crashing on unsupported AMD iGPUs

* Remove cuda device filtering

This interferes with mixed setups

We already run flash attention on CPUs in cases where we have
partial offloading but were disabling it if running on pure CPU,
 which is unnecessary.

* add build to .dockerignore

* test: only build one arch

* add build to .gitignore

* fix ccache path

* filter amdgpu targets

* only filter if autodetecting

* Don't clobber gpu list for default runner

This ensures the GPU specific environment variables are set properly

* explicitly set CXX compiler for HIP

* Update build_windows.ps1

This isn't complete, but is close.  Dependencies are missing, and it only builds the "default" preset.

* build: add ollama subdir

* add .git to .dockerignore

* docs: update development.md

* update build_darwin.sh

* remove unused scripts

* llm: add cwd and build/lib/ollama to library paths

* default DYLD_LIBRARY_PATH to LD_LIBRARY_PATH in runner on macOS

* add additional cmake output vars for msvc

* interim edits to make server detection logic work with dll directories like lib/ollama/cuda_v12

* remove unncessary filepath.Dir, cleanup

* add hardware-specific directory to path

* use absolute server path

* build: linux arm

* cmake install targets

* remove unused files

* ml: visit each library path once

* build: skip cpu variants on arm

* build: install cpu targets

* build: fix workflow

* shorter names

* fix rocblas install

* docs: clean up development.md

* consistent build dir removal in development.md

* silence -Wimplicit-function-declaration build warnings in ggml-cpu

* update readme

* update development readme

* llm: update library lookup logic now that there is one runner (#8587)

* tweak development.md

* update docs

* add windows cuda/rocm tests

---------
Co-authored-by: jmorganca <jmorganca@gmail.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>

* llama: wire up builtin runner

This adds a new entrypoint into the ollama CLI to run the cgo built runner.
On Mac arm64, this will have GPU support, but on all other platforms it will
be the lowest common denominator CPU build.  After we fully transition
to the new Go runners more tech-debt can be removed and we can stop building
the "default" runner via make and rely on the builtin always.

* build: Make target improvements

Add a few new targets and help for building locally.
This also adjusts the runner lookup to favor local builds, then
runners relative to the executable, and finally payloads.

* Support customized CPU flags for runners

This implements a simplified custom CPU flags pattern for the runners.
When built without overrides, the runner name contains the vector flag
we check for (AVX) to ensure we don't try to run on unsupported systems
and crash.  If the user builds a customized set, we omit the naming
scheme and don't check for compatibility.  This avoids checking
requirements at runtime, so that logic has been removed as well.  This
can be used to build GPU runners with no vector flags, or CPU/GPU
runners with additional flags (e.g. AVX512) enabled.

* Use relative paths

If the user checks out the repo in a path that contains spaces, make gets
really confused so use relative paths for everything in-repo to avoid breakage.

* Remove payloads from main binary

* install: clean up prior libraries

This removes support for v0.3.6 and older versions (before the tar bundle)
and ensures we clean up prior libraries before extracting the bundle(s).
Without this change, runners and dependent libraries could leak when we
update and lead to subtle runtime errors.

This adds support for the Jetson JetPack variants into the Go runner

Until we have full NUMA support, this adjusts the default thread selection
algorithm to count up the number of performance cores across all sockets.

Cleaning up go package naming

On windows, detect large multi-socket systems and reduce to the number of cores
in one socket for best performance

* Expose GPU discovery failure information

* Remove exposed API for now

The recent cuda variant changes uncovered a bug in ByLibrary
which failed to group by common variant for GPU types.

Based on compute capability and driver version, pick
v12 or v11 cuda variants.

This adds new variants for arm64 specific to Jetson platforms

* llm: avoid loading model if system memory is too small

* update log

* Instrument swap free space

On linux and windows, expose how much swap space is available
so we can take that into consideration when scheduling models

* use `systemSwapFreeMemory` in check

---------
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>

This adds logic to detect skew between the driver and
management library which can be attributed to OS overhead
and records that so we can adjust subsequent management
library free VRAM updates and avoid OOM scenarios.

Until ROCm v6.2 ships, we wont be able to get accurate free memory
reporting on windows, which makes automatic concurrency too risky.
Users can still opt-in but will need to pay attention to model sizes otherwise they may thrash/page VRAM or cause OOM crashes.
All other platforms and GPUs have accurate VRAM reporting wired
up now, so we can turn on concurrency by default.

Implement support for GPU env var workarounds, and leverage
this for the Vega RX 56 which needs
HSA_ENABLE_SDMA=0 set to work properly

Still not complete, needs some refinement to our prediction to understand the
discrete GPUs available space so we can see how many layers fit in each one
since we can't split one layer across multiple GPUs we can't treat free space
as one logical block

Now that we call the GPU discovery routines many times to
update memory, this splits initial discovery from free memory
updating.

This cleans up the logging for GPU discovery a bit, and can
serve as a foundation to report GPU information in a future UX.

This change adds support for multiple concurrent requests, as well as
loading multiple models by spawning multiple runners. The default
settings are currently set at 1 concurrent request per model and only 1
loaded model at a time, but these can be adjusted by setting
OLLAMA_NUM_PARALLEL and OLLAMA_MAX_LOADED_MODELS.

count each layer independently when deciding gpu offloading

This wires up some new logic to start using sysfs to discover AMD GPU
information and detects old cards we can't yet support so we can fallback to CPU mode.

In some cases we may want multiple variants for a given GPU type or CPU.
This adds logic to have an optional Variant which we can use to select
an optimal library, but also allows us to try multiple variants in case
some fail to load.

This can be useful for scenarios such as ROCm v5 vs v6 incompatibility
or potentially CPU features.

This refines the gpu package error handling and fixes a bug with the
system memory lookup on windows.

Refactor where we store build outputs, and support a fully dynamic loading
model on windows so the base executable has no special dependencies thus
doesn't require a special PATH.

This switches the default llama.cpp to be CPU based, and builds the GPU variants
as dynamically loaded libraries which we can select at runtime.

This also bumps the ROCm library to version 6 given 5.7 builds don't work
on the latest ROCm library that just shipped.